Grade control in underground mines aims to deliver quality tonnes to the process plant via the accurate definition of ore and waste. It comprises a decision-making process including data collection and interpretation; local estimation; development and mining supervision; ore and waste destination tracking; and stockpile management. The foundation of any grade control programme is that of high-quality samples collected in a geological context. The requirement for quality samples has long been recognised, where they should be representative and fit-for-purpose. Once a sampling error is introduced, it propagates through all subsequent processes contributing to data uncertainty, which leads to poor decisions and financial loss. Proper application of the Theory of Sampling reduces errors during sample collection, preparation, and assaying. To achieve quality, sampling techniques must minimise delimitation, extraction, and preparation errors. Underground sampling methods include linear (chip and channel), grab (broken rock), and drill-based samples. Grade control staff should be well-trained and motivated, and operating staff should understand the critical need for grade control. Sampling must always be undertaken with a strong focus on safety and alternatives sought if the risk to humans is high. A quality control/quality assurance programme must be implemented, particularly when samples contribute to a reserve estimate. This paper assesses grade control sampling with emphasis on underground gold operations and presents recommendations for optimal practice through the application of the Theory of Sampling.

Grade control aims to deliver adequately defined tonnes of ore to the process plant. The foundation of any grade control programme is collecting high-quality samples within a geological context. The requirement for quality samples has long been recognised, in that these should be representative and fit-for-purpose. Correct application of the Theory of Sampling reduces sampling errors across the grade control process, in which errors can propagate from sample collection through sample preparation to assay results. This contribution presents three case studies which are based on coarse gold-dominated orebodies. These illustrate the challenges and potential solutions to achieve representative sampling and build on the content of a previous publication. Solutions ranging from bulk samples processed through a plant to whole-core sampling and assaying using bulk leaching, are discussed. These approaches account for the nature of the mineralisation, where extreme gold particle-clustering effects render the analysis of small-scale samples highly unrepresentative. Furthermore, the analysis of chip samples, which generally yield a positive bias due to over-sampling of quartz vein material, is discussed.